
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
from datetime import datetime


pd.options.display.float_format = '{:.3f}'.format


# VARS
dir_diversity_output = '../results_diversity'
dir_reads_fastq = '../data/control_sample/'
fastq_basename = 'POOL.fastq.gz'
dir_results_profiling = '../results_profiling'
pools_file="../data/EM_EVPools/samples_profiling.txt"


today = datetime.today().strftime('%Y-%m-%d')
os.makedirs(f'{dir_diversity_output}/{today}', exist_ok=True)


# GENERAL VARIABLES
POOL_list = !cat {pools_file}
POOL_list += ['ACIDOLA', 'BLACTIS']


# Attributes 
cutoff_NA_ratio = 0.35


# Create the pooled dataframe. We are going to separate mean and percentage to have some representation of two variables.

# The dataframe we are going to show has values of all samples using the raw dataframe, and not the cutoff one. However, to do the filtering we 
# are going to use the cutoff one, because for some species that have discordant read values, in many pools they are discarded, but not in all of them; so
# when doing the heatmap here, they appear on top, when in reality they should have been discarded for not appearing in many datasets

list_dfs_means_raw, list_dfs_per_raw = [], []
list_dfs_means_cutoff, list_dfs_per_raw_cutoff = [], []

list_selected_index = []


for POOL in POOL_list:
    df_POOL_cutoff = pd.read_csv(f'{dir_diversity_output}/{today}/{POOL}.diversity_cutoff.tsv', sep='\t', index_col='Unnamed: 0')
    df_POOL_cutoff.reset_index(inplace=True)
    df_POOL_cutoff = df_POOL_cutoff[['index', 'name', 'mean (%)', 'mean']].rename(columns = {'mean (%)': f'mean (%) {POOL}', 'mean': f'mean {POOL}'})
    df_POOL_cutoff['taxon - genus'] = df_POOL_cutoff['index'].astype(str) + ' - ' + df_POOL_cutoff['name']
    df_POOL_cutoff = df_POOL_cutoff.set_index('taxon - genus')

    df_POOL_raw = pd.read_csv(f'{dir_diversity_output}/{today}/{POOL}.diversity_raw.tsv', sep='\t', index_col='Unnamed: 0')
    df_POOL_raw.reset_index(inplace=True)
    df_POOL_raw = df_POOL_raw[['index', 'name', 'mean (%)', 'mean']].rename(columns = {'mean (%)': f'mean (%) {POOL}', 'mean': f'mean {POOL}'})
    df_POOL_raw['taxon - genus'] = df_POOL_raw['index'].astype(str) + ' - ' + df_POOL_raw['name']
    df_POOL_raw = df_POOL_raw.set_index('taxon - genus')

    list_dfs_means_raw.append(df_POOL_raw[f'mean {POOL}'])
    list_dfs_per_raw.append(df_POOL_raw[f'mean (%) {POOL}'])

    list_dfs_means_cutoff.append(df_POOL_cutoff[f'mean {POOL}'])
    list_dfs_per_raw_cutoff.append(df_POOL_cutoff[f'mean (%) {POOL}'])

    list_selected_index += df_POOL_cutoff.index.tolist()


selected_index = list(set(list_selected_index))
df_mean_raw, df_per_raw = pd.concat(list_dfs_means_raw, axis=1), pd.concat(list_dfs_per_raw, axis=1)
df_mean_raw = df_mean_raw.loc[selected_index]
df_per_raw = df_per_raw.loc[selected_index]

df_mean_cutoff, df_per_cutoff = pd.concat(list_dfs_means_cutoff, axis=1), pd.concat(list_dfs_per_raw_cutoff, axis=1)
df_mean_cutoff = df_mean_cutoff.loc[selected_index]
df_per_cutoff = df_per_cutoff.loc[selected_index]


# NA cut to keep only species that have only a set of values as NAs
nonNA_index = df_mean_cutoff[df_mean_cutoff.isna().sum(1) < int(cutoff_NA_ratio * len(POOL_list))].index

# Then we order by the median of the values (using mean skewed some species much present in a few samples)
df_mean_cutoff_nonNA = df_mean_cutoff.loc[nonNA_index]
df_mean_cutoff_nonNA = df_mean_cutoff_nonNA.assign(m=df_mean_cutoff_nonNA.median(axis=1)).sort_values('m', ascending=False).drop('m', axis=1)
df_mean_cutoff_nonNA.to_csv(f'{dir_diversity_output}/{today}/df_mean_cutoff_nonNA.tsv', sep='\t')


df_per_cutoff_nonNA = df_per_cutoff.loc[nonNA_index]
df_per_cutoff_nonNA = df_per_cutoff_nonNA.assign(m=df_per_cutoff_nonNA.median(axis=1)).sort_values('m', ascending=False).drop('m', axis=1)
df_per_cutoff_nonNA.to_csv(f'{dir_diversity_output}/{today}/df_per_cutoff_nonNA.tsv', sep='\t')


# Then do the same in raw, but only with cutoff samples
df_mean_raw_cutoffindex_nonNA = df_mean_raw.loc[df_mean_cutoff_nonNA.index.values]
df_mean_raw_cutoffindex_nonNA.to_csv(f'{dir_diversity_output}/{today}/df_mean_raw_cutoffindex_nonNA.tsv', sep='\t')


df_per_raw_cutoffindex_nonNA = df_per_raw.loc[df_per_cutoff_nonNA.index.values]
df_per_raw_cutoffindex_nonNA.to_csv(f'{dir_diversity_output}/{today}/df_per_raw_cutoffindex_nonNA.tsv', sep='\t')


# Then do the same in raw as with cutoff
df_mean_raw_nonNA = df_mean_raw
df_mean_raw_nonNA = df_mean_raw_nonNA.assign(m=df_mean_raw_nonNA.median(axis=1)).sort_values('m', ascending=False).drop('m', axis=1)
df_mean_raw_nonNA.to_csv(f'{dir_diversity_output}/{today}/df_mean_raw_nonNA.tsv', sep='\t')


df_per_raw_nonNA = df_per_raw
df_per_raw_nonNA = df_per_raw_nonNA.assign(m=df_per_raw_nonNA.median(axis=1)).sort_values('m', ascending=False).drop('m', axis=1)
df_per_raw_nonNA.to_csv(f'{dir_diversity_output}/{today}/df_per_raw_nonNA.tsv', sep='\t')


df_mean_raw_nonNA


df_mean_raw_cutoffindex_nonNA


df_mean_cutoff_nonNA


N = 25
fig, ax = plt.subplots(1, 1, figsize=(9, 7))
sns.heatmap(np.log10(df_mean_raw_nonNA.iloc[1:N, :]), yticklabels=True, annot=True, cmap='Blues')
plt.title('log10 mean counts')
plt.tight_layout()

fig, ax = plt.subplots(1, 1, figsize=(9, 7))
sns.heatmap(np.log10(df_mean_raw_cutoffindex_nonNA.iloc[1:N, :]), yticklabels=True, annot=True, cmap='Blues')
plt.title('log10 mean counts')
plt.tight_layout()


fig, ax = plt.subplots(1, 1, figsize=(9, 7))
sns.heatmap(np.log10(df_mean_cutoff_nonNA.iloc[1:N, :]), yticklabels=True, annot=True, cmap='Blues')
plt.title('log10 mean counts')
plt.tight_layout()


df_mean_raw_cutoffindex_nonNA.iloc[1:N, :]


N = 100

fig, ax = plt.subplots(1, 1, figsize=(9, 22))
sns.heatmap(np.log10(df_mean_raw_cutoffindex_nonNA.iloc[1:N, :]), yticklabels=True, annot=True, cmap='Blues')
plt.title('log10 mean counts')
plt.tight_layout()
plt.savefig(f'{dir_diversity_output}/{today}/heatmap_mean_nonNA_annot.png', dpi=300)



fig, ax = plt.subplots(1, 1, figsize=(9, 22))
sns.heatmap(np.log10(df_mean_raw_cutoffindex_nonNA.iloc[1:N, :]), yticklabels=True, annot=False, cmap='Blues')
plt.title('log10 mean counts')
plt.tight_layout()
plt.savefig(f'{dir_diversity_output}/{today}/heatmap_mean_nonNA.png', dpi=300)


N = 100

fig, ax = plt.subplots(1, 1, figsize=(9, 22))
sns.heatmap(np.log10(df_per_raw_cutoffindex_nonNA.iloc[1:N, :]), yticklabels=True, annot=True, cmap='Blues')
plt.title('log10 percentage counts')
plt.tight_layout()
plt.savefig(f'{dir_diversity_output}/{today}/heatmap_per_nonNA_annot.png', dpi=300)



fig, ax = plt.subplots(1, 1, figsize=(9, 22))
sns.heatmap(np.log10(df_per_raw_cutoffindex_nonNA.iloc[1:N, :]), yticklabels=True, annot=False, cmap='Blues')
plt.title('log10 percentage counts')
plt.tight_layout()
plt.savefig(f'{dir_diversity_output}/{today}/heatmap_per_nonNA.png', dpi=300)


## Try a quick wilcoxon test
from scipy.stats import mannwhitneyu, ttest_ind


# RR [POOL 1-4] vs HC [POOL 8-12]

list_pvals_mannwhitney = []
list_pvals_ttest = []
L2FC = []

for row in range(len(df_mean_raw_cutoffindex_nonNA)):
    condition_vals = df_mean_raw_cutoffindex_nonNA.iloc[row, :4].values
    reference_vals = df_mean_raw_cutoffindex_nonNA.iloc[row, 8:12].values

    res = mannwhitneyu(condition_vals, reference_vals)
    list_pvals_mannwhitney.append(res.pvalue)

    res = ttest_ind(condition_vals, reference_vals)
    list_pvals_ttest.append(res.pvalue)

    L2FC.append(np.log2(condition_vals.mean() / reference_vals.mean()))




df_pval = df_mean_raw_cutoffindex_nonNA.iloc[:, [0,1,2,3,8,9,10,11]]
df_pval['log2FC'] = L2FC
df_pval['pval_ttest'] = list_pvals_ttest
df_pval['pval_MW'] = list_pvals_mannwhitney


df_pval = df_pval.sort_values(by=['pval_MW', 'pval_ttest'])
display(df_pval.iloc[:15])

df_pval_pos = df_pval[(df_pval['pval_MW'] < 0.05)]


fig, ax = plt.subplots(1, 1, figsize=(9, 2))
sns.heatmap(np.log10(df_pval_pos.iloc[:, :-3]), yticklabels=True, annot=True, cmap='Blues')
plt.title('log10 percentage counts')
plt.tight_layout()

/tmp/ipykernel_2735545/465228851.py:23: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['log2FC'] = L2FC
/tmp/ipykernel_2735545/465228851.py:24: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['pval_ttest'] = list_pvals_ttest
/tmp/ipykernel_2735545/465228851.py:25: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['pval_MW'] = list_pvals_mannwhitney


# SP vs HC

list_pvals_mannwhitney = []
list_pvals_ttest = []
L2FC = []

for row in range(len(df_mean_raw_cutoffindex_nonNA)):
    condition_vals = df_per_raw_cutoffindex_nonNA.iloc[row, 4:8].values
    reference_vals = df_per_raw_cutoffindex_nonNA.iloc[row, 8:12].values

    res = mannwhitneyu(condition_vals, reference_vals)
    list_pvals_mannwhitney.append(res.pvalue)

    res = ttest_ind(condition_vals, reference_vals)
    list_pvals_ttest.append(res.pvalue)

    L2FC.append(np.log2(condition_vals.mean() / reference_vals.mean()))




df_pval = df_mean_raw_cutoffindex_nonNA.iloc[:, [4,5,6,7,8,9,10,11]]
df_pval['log2FC'] = L2FC
df_pval['pval_ttest'] = list_pvals_ttest
df_pval['pval_MW'] = list_pvals_mannwhitney


df_pval = df_pval.sort_values(by=['pval_MW', 'pval_ttest'])
display(df_pval.iloc[:15])

df_pval_pos = df_pval[(df_pval['pval_MW'] < 0.05)]


fig, ax = plt.subplots(1, 1, figsize=(9, 3))
sns.heatmap(np.log10(df_pval_pos.iloc[:, :-3]), yticklabels=True, annot=True, cmap='Blues')
plt.title('log10 percentage counts')
plt.tight_layout()

/tmp/ipykernel_2735545/1233428652.py:23: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['log2FC'] = L2FC
/tmp/ipykernel_2735545/1233428652.py:24: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['pval_ttest'] = list_pvals_ttest
/tmp/ipykernel_2735545/1233428652.py:25: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['pval_MW'] = list_pvals_mannwhitney


# RR vs SP

list_pvals_mannwhitney = []
list_pvals_ttest = []
L2FC = []

for row in range(len(df_mean_raw_cutoffindex_nonNA)):
    condition_vals = df_per_raw_cutoffindex_nonNA.iloc[row, :4].values
    reference_vals = df_per_raw_cutoffindex_nonNA.iloc[row, 4:8].values

    res = mannwhitneyu(condition_vals, reference_vals)
    list_pvals_mannwhitney.append(res.pvalue)

    res = ttest_ind(condition_vals, reference_vals)
    list_pvals_ttest.append(res.pvalue)

    L2FC.append(np.log2(condition_vals.mean() / reference_vals.mean()))




df_pval = df_mean_raw_cutoffindex_nonNA.iloc[:, [0,1,2,3,4,5,6,7]]
df_pval['log2FC'] = L2FC
df_pval['pval_ttest'] = list_pvals_ttest
df_pval['pval_MW'] = list_pvals_mannwhitney


df_pval = df_pval.sort_values(by=['pval_MW', 'pval_ttest'])
display(df_pval.iloc[:15])

df_pval_pos = df_pval[(df_pval['pval_MW'] < 0.05)]


fig, ax = plt.subplots(1, 1, figsize=(9, 5))
sns.heatmap(np.log10(df_pval_pos.iloc[:, :-3]), yticklabels=True, annot=True, cmap='Blues')
plt.title('log10 percentage counts')
plt.tight_layout()

/tmp/ipykernel_2735545/248738686.py:23: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['log2FC'] = L2FC
/tmp/ipykernel_2735545/248738686.py:24: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['pval_ttest'] = list_pvals_ttest
/tmp/ipykernel_2735545/248738686.py:25: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['pval_MW'] = list_pvals_mannwhitney


# SEX

list_pvals_mannwhitney = []
list_pvals_ttest = []
L2FC = []

for row in range(len(df_mean_raw_cutoffindex_nonNA)):
    condition_vals = df_per_raw_cutoffindex_nonNA.iloc[row, [2,3, 6,7, 10,11]].values  #FEMALE
    reference_vals = df_per_raw_cutoffindex_nonNA.iloc[row, [0,1, 4,5, 8,9]].values  #MALE

    res = mannwhitneyu(condition_vals, reference_vals)
    list_pvals_mannwhitney.append(res.pvalue)

    res = ttest_ind(condition_vals, reference_vals)
    list_pvals_ttest.append(res.pvalue)

    L2FC.append(np.log2(condition_vals.mean() / reference_vals.mean()))




df_pval = df_mean_raw_cutoffindex_nonNA.iloc[:, [0,1,4,5,8,9,2,3,6,7,10,11]]
df_pval['log2FC'] = L2FC
df_pval['pval_ttest'] = list_pvals_ttest
df_pval['pval_MW'] = list_pvals_mannwhitney


df_pval = df_pval.sort_values(by=['pval_MW', 'pval_ttest'])
display(df_pval.iloc[:15])

df_pval_pos = df_pval[(df_pval['pval_MW'] < 0.05)]

try:
    fig, ax = plt.subplots(1, 1, figsize=(9, 5))
    sns.heatmap(np.log10(df_pval_pos.iloc[:, :-3]), yticklabels=True, annot=True, cmap='Blues')
    plt.title('log10 percentage counts')
    plt.tight_layout()
except:
    print('NO SIGNIFICANT SAMPLES')

/tmp/ipykernel_2735545/3247866785.py:23: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_pval['log2FC'] = L2FC

NO SIGNIFICANT SAMPLES

	mean POOL1	mean POOL2	mean POOL3	mean POOL4	mean POOL5	mean POOL6	mean POOL7	mean POOL8	mean POOL9	mean POOL10	mean POOL11	mean POOL12	mean ACIDOLA	mean BLACTIS
taxon - genus
9605 - Homo	1709929.998	1933932.908	1288302.626	1523916.538	1034480.997	570833.058	1050951.429	1048025.882	1049918.995	1260704.944	1109145.249	1321520.166	2305.690	288.340
36034 - Saccharomycodes	81123.066	59855.630	47695.527	62199.371	45876.058	19458.106	37345.758	30799.966	52090.312	61101.690	34952.457	65185.823	6.961	3.252
1912216 - Cutibacterium	11290.359	51032.855	18299.991	21386.945	16490.551	22427.767	23451.886	21068.794	32960.381	9649.674	17538.432	38091.691	480.752	73.982
37914 - Dietzia	10825.673	52825.661	17149.516	8769.884	20165.245	28191.640	45763.874	15913.459	46163.752	8122.847	6675.907	40384.093	NaN	NaN
13687 - Sphingomonas	4688.349	29033.744	12101.603	6543.925	17594.253	25613.385	28941.491	20731.628	31299.074	6169.392	6310.884	29744.999	13.303	1.084
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2948645 - Burzaovirus	NaN	NaN	1.976	NaN	NaN	283.320	0.866	NaN	NaN	NaN	NaN	NaN	1.856	NaN
1184396 - Mesotoga	1.705	3.427	2.964	NaN	1.079	1080.139	1.731	9.744	1.125	1.313	NaN	NaN	NaN	1.084
499228 - Tepidanaerobacter	NaN	1.142	20.749	NaN	1.079	105.992	1.443	313.814	1.687	6.420	1.157	1.194	NaN	NaN
2082587 - Parolsenella	2.558	1.142	56.812	1.023	1.079	1289.236	0.866	540.271	1.125	0.875	8.101	5.971	1.856	1.084
51196 - Syntrophobotulus	NaN	1.142	NaN	1.023	74.964	428.590	0.866	112.220	1.125	NaN	5.208	1.194	NaN	NaN

	mean POOL1	mean POOL2	mean POOL3	mean POOL4	mean POOL5	mean POOL6	mean POOL7	mean POOL8	mean POOL9	mean POOL10	mean POOL11	mean POOL12	mean ACIDOLA	mean BLACTIS
taxon - genus
9605 - Homo	1709929.998	1933932.908	1288302.626	1523916.538	1034480.997	570833.058	1050951.429	1048025.882	1049918.995	1260704.944	1109145.249	1321520.166	2305.690	288.340
1912216 - Cutibacterium	11290.359	51032.855	18299.991	21386.945	16490.551	22427.767	23451.886	21068.794	32960.381	9649.674	17538.432	38091.691	480.752	73.982
13687 - Sphingomonas	4688.349	29033.744	12101.603	6543.925	17594.253	25613.385	28941.491	20731.628	31299.074	6169.392	6310.884	29744.999	13.303	1.084
37914 - Dietzia	10825.673	52825.661	17149.516	8769.884	20165.245	28191.640	45763.874	15913.459	46163.752	8122.847	6675.907	40384.093	NaN	NaN
469 - Acinetobacter	2772.345	11158.895	7052.537	9496.731	13154.365	23846.245	14577.205	80605.920	21943.776	15249.816	3400.060	15808.468	36.505	45.527
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
724 - Haemophilus	97.470	969.743	204.851	294.488	659.759	4195.865	1048.730	519.103	439.022	248.185	263.472	6373.544	0.928	NaN
482 - Neisseria	440.461	2410.079	156.767	343.314	584.614	1009.670	356.020	1045.263	827.701	194.054	362.226	15916.843	3.248	NaN
497 - Psychrobacter	193.519	255.476	673.505	414.124	3318.928	455.738	484.118	1028.127	1373.220	291.227	289.318	1429.082	0.928	NaN
84567 - Pedobacter	138.674	359.418	362.606	390.606	602.592	566.640	384.005	526.159	774.733	201.349	329.436	723.696	2.784	NaN
2040 - Aeromicrobium	57.970	664.009	265.779	348.000	356.306	590.467	604.715	283.575	578.238	221.193	263.472	807.292	NaN	1.084

	mean POOL1	mean POOL2	mean POOL3	mean POOL4	mean POOL5	mean POOL6	mean POOL7	mean POOL8	mean POOL9	mean POOL10	mean POOL11	mean POOL12	mean ACIDOLA	mean BLACTIS
taxon - genus
9605 - Homo	1709929.998	1933932.908	1288302.626	1523916.538	1034480.997	570833.058	1050951.429	1048025.882	1049918.995	1260704.944	1109145.249	1321520.166	2305.690	288.340
1912216 - Cutibacterium	11290.359	51032.855	18299.991	21386.945	16490.551	22427.767	23451.886	21068.794	32960.381	9649.674	17538.432	38091.691	480.752	NaN
13687 - Sphingomonas	4688.349	29033.744	12101.603	6543.925	17594.253	25613.385	28941.491	20731.628	31299.074	6169.392	6310.884	29744.999	NaN	NaN
37914 - Dietzia	10825.673	52825.661	17149.516	8769.884	20165.245	28191.640	45763.874	15913.459	46163.752	8122.847	6675.907	40384.093	NaN	NaN
469 - Acinetobacter	2772.345	11158.895	7052.537	9496.731	13154.365	23846.245	14577.205	80605.920	21943.776	15249.816	3400.060	15808.468	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
724 - Haemophilus	NaN	969.743	204.851	294.488	659.759	4195.865	1048.730	519.103	439.022	248.185	263.472	6373.544	NaN	NaN
482 - Neisseria	440.461	2410.079	156.767	343.314	584.614	1009.670	356.020	1045.263	827.701	194.054	362.226	15916.843	NaN	NaN
497 - Psychrobacter	193.519	255.476	673.505	414.124	3318.928	455.738	484.118	1028.127	1373.220	291.227	289.318	1429.082	NaN	NaN
84567 - Pedobacter	138.674	359.418	362.606	390.606	602.592	566.640	384.005	526.159	774.733	201.349	329.436	723.696	NaN	NaN
2040 - Aeromicrobium	NaN	664.009	265.779	348.000	356.306	590.467	604.715	283.575	578.238	221.193	263.472	807.292	NaN	NaN

	mean POOL1	mean POOL2	mean POOL3	mean POOL4	mean POOL5	mean POOL6	mean POOL7	mean POOL8	mean POOL9	mean POOL10	mean POOL11	mean POOL12	mean ACIDOLA	mean BLACTIS
taxon - genus
1912216 - Cutibacterium	11290.359	51032.855	18299.991	21386.945	16490.551	22427.767	23451.886	21068.794	32960.381	9649.674	17538.432	38091.691	480.752	73.982
13687 - Sphingomonas	4688.349	29033.744	12101.603	6543.925	17594.253	25613.385	28941.491	20731.628	31299.074	6169.392	6310.884	29744.999	13.303	1.084
37914 - Dietzia	10825.673	52825.661	17149.516	8769.884	20165.245	28191.640	45763.874	15913.459	46163.752	8122.847	6675.907	40384.093	NaN	NaN
469 - Acinetobacter	2772.345	11158.895	7052.537	9496.731	13154.365	23846.245	14577.205	80605.920	21943.776	15249.816	3400.060	15808.468	36.505	45.527
12916 - Acidovorax	3195.614	11895.625	13657.993	4548.717	13717.947	19686.625	30035.517	10773.411	33610.055	5720.076	4023.829	25862.296	3.094	NaN
1386 - Bacillus	2112.507	15043.006	7450.383	4710.447	7874.768	48486.421	12120.551	15452.481	17655.743	4005.104	4657.627	20831.152	1099.170	76.963
106589 - Cupriavidus	2594.172	14180.917	12334.036	3906.569	11694.717	20391.026	26735.047	9139.243	30993.361	5864.085	3695.164	23304.578	9.745	NaN
1716 - Corynebacterium	3982.049	12378.212	4941.618	5210.548	12241.310	13249.976	19116.029	7387.899	19589.483	4648.328	21728.329	9789.607	41.764	5.781
1678 - Bifidobacterium	715.891	13496.158	10190.759	347.404	1395.201	405656.412	12225.569	9068.686	9712.665	333.832	189.792	1440.626	82.832	1746265.778
286 - Pseudomonas	1579.691	7116.301	8640.791	2966.866	20818.262	31180.146	5227.350	215000.525	113899.851	48460.663	5460.869	8496.268	46.405	2.891
196013 - Caldimonas	2271.286	8988.396	7712.293	2637.356	8077.819	10798.434	15713.642	5392.375	17120.724	3553.162	2544.259	14416.905	1.856	NaN
1827 - Rhodococcus	2528.316	10710.003	5306.694	2767.728	8542.706	9914.901	15098.469	5647.391	15276.607	2576.179	3059.533	18296.921	20.418	NaN
57493 - Kocuria	1031.744	15190.352	2149.453	9120.696	54524.200	31542.960	5599.527	6025.127	8896.870	2415.975	2836.470	7362.656	19.799	1.445
909656 - Phocaeicola	197.497	6149.319	21086.145	540.917	910.719	919244.317	15336.056	179271.849	11252.007	165.238	344.481	1222.084	1.856	NaN
407 - Methylobacterium	1188.605	5606.004	5145.645	2551.464	6594.710	14681.088	7832.803	5201.617	11522.001	2332.809	2805.802	14268.225	2.475	1.084
1301 - Streptococcus	1968.220	69050.196	2013.929	2781.277	4284.027	95039.999	5335.541	10572.910	9364.673	3412.874	4222.012	54646.247	68.679	1.445
265 - Paracoccus	903.442	4292.073	774.860	7220.583	10034.174	7797.582	1207.410	60205.409	2228.858	4524.892	10290.737	997.471	3.403	2.710
1883 - Streptomyces	1273.358	8505.714	3106.110	3501.478	4198.816	7967.039	5357.828	3827.253	7735.333	2286.922	2236.425	7807.801	70.535	7.227
1279 - Staphylococcus	2020.010	5626.945	1953.988	3297.654	2980.779	13478.350	4838.007	4716.617	7185.501	1506.838	2477.426	5648.654	146.174	9.033
816 - Bacteroides	227.335	3974.156	9193.922	306.758	823.350	436335.983	5810.066	208378.933	4382.530	221.047	379.874	2475.217	1.237	NaN
572511 - Blautia	468.309	6134.470	29231.111	737.754	1630.521	964184.112	20585.045	43023.591	20057.566	158.745	289.028	617.411	2.784	1.084
283 - Comamonas	1886.167	4940.377	4192.446	782.234	2140.800	3336.158	6658.211	4255.640	5241.824	2053.327	1709.288	3851.952	2.166	NaN
1269 - Micrococcus	1110.814	8954.701	2225.037	6058.480	9975.120	4722.578	2774.230	2128.828	2855.938	10248.032	2784.971	5304.719	10.828	1.084
57494 - Nesterenkonia	1232.153	4312.252	3382.017	1572.989	4056.707	5007.053	9124.963	3493.616	8785.685	1613.714	1117.537	9788.213	NaN	NaN

	mean POOL1	mean POOL2	mean POOL3	mean POOL4	mean POOL9	mean POOL10	mean POOL11	mean POOL12	log2FC	pval_ttest	pval_MW
taxon - genus
53457 - Janibacter	242.680	101.657	476.888	486.383	802.670	538.391	526.558	604.673	-0.919	0.043	0.029
9605 - Homo	1709929.998	1933932.908	1288302.626	1523916.538	1049918.995	1260704.944	1109145.249	1321520.166	0.445	0.030	0.057
125216 - Roseomonas	174.337	458.600	707.427	485.701	2409.323	572.095	1047.715	1634.288	-1.633	0.058	0.057
1696 - Brevibacterium	334.182	519.995	905.362	1117.112	1698.713	1061.900	2918.636	1184.068	-1.255	0.073	0.057
237 - Flavobacterium	229.324	829.250	590.182	388.220	894.731	1398.285	391.157	1552.882	-1.057	0.110	0.114
165696 - Novosphingobium	210.782	770.711	370.016	277.105	1370.783	406.857	525.111	1396.340	-1.184	0.129	0.114
165695 - Sphingobium	759.582	2048.377	1756.712	506.663	3202.806	2728.723	818.769	2891.203	-0.927	0.133	0.114
29580 - Janthinobacterium	230.745	317.917	601.050	201.438	5777.125	635.856	271.187	736.236	-2.457	0.292	0.114
286 - Pseudomonas	1579.691	7116.301	8640.791	2966.866	113899.851	48460.663	5460.869	8496.268	-3.118	0.174	0.200
469 - Acinetobacter	2772.345	11158.895	7052.537	9496.731	21943.776	15249.816	3400.060	15808.468	-0.888	0.181	0.200
1578 - Lactobacillus	673.195	4523.563	821.709	1159.547	3442.707	1517.125	16327.927	2874.484	-1.751	0.279	0.200
89966 - Hymenobacter	248.647	3774.268	603.355	408.670	5480.882	719.606	727.344	2339.872	-0.880	0.479	0.200
1716 - Corynebacterium	3982.049	12378.212	4941.618	5210.548	19589.483	4648.328	21728.329	9789.607	-1.072	0.154	0.343
1822464 - Paraburkholderia	177.747	640.784	1001.859	388.902	1729.088	584.789	411.217	1560.048	-0.956	0.220	0.343
497 - Psychrobacter	193.519	255.476	673.505	414.124	1373.220	291.227	289.318	1429.082	-1.138	0.221	0.343

	mean POOL5	mean POOL6	mean POOL7	mean POOL8	mean POOL9	mean POOL10	mean POOL11	mean POOL12	log2FC	pval_ttest	pval_MW
taxon - genus
338 - Xanthomonas	1603.645	1703.963	2571.048	974.453	674.235	610.614	381.610	458.182	1.690	0.012	0.029
59732 - Chryseobacterium	2593.373	3726.192	2432.708	3218.189	1947.708	557.213	499.169	2170.791	1.210	0.019	0.029
75 - Caulobacter	982.627	1434.362	1276.652	4758.112	902.512	303.119	298.865	913.577	1.805	0.146	0.029
9605 - Homo	1034480.997	570833.058	1050951.429	1048025.882	1049918.995	1260704.944	1109145.249	1321520.166	-0.356	0.102	0.057
169133 - Citricoccus	1091.029	1092.269	1554.486	587.645	2314.825	351.048	270.416	1788.143	4.077	0.279	0.057
572511 - Blautia	1630.521	964184.112	20585.045	43023.591	20057.566	158.745	289.028	617.411	5.607	0.326	0.057
52972 - Polaromonas	1054.176	627.578	1019.014	611.836	713.985	251.541	256.143	582.779	0.876	0.066	0.114
517 - Bordetella	792.250	4083.736	1213.180	4430.690	945.261	511.253	283.145	1150.032	1.864	0.096	0.114
1839 - Nocardioides	3947.496	4966.548	4746.983	2615.424	3319.241	1035.856	1979.511	3760.594	0.689	0.108	0.114
1485 - Clostridium	1169.229	135619.091	6065.036	218546.808	3276.679	609.885	620.876	1410.372	5.932	0.144	0.114
816 - Bacteroides	823.350	436335.983	5810.066	208378.933	4382.530	221.047	379.874	2475.217	6.448	0.170	0.114
909656 - Phocaeicola	910.719	919244.317	15336.056	179271.849	11252.007	165.238	344.481	1222.084	6.424	0.252	0.114
665874 - Limnohabitans	747.487	1854.432	2146.507	289.287	3800.168	1221.228	1971.217	3390.386	-1.043	0.124	0.200
165695 - Sphingobium	4139.761	3742.727	2614.973	46364.752	3202.806	2728.723	818.769	2891.203	2.560	0.314	0.200
1678 - Bifidobacterium	1395.201	405656.412	12225.569	9068.686	9712.665	333.832	189.792	1440.626	5.197	0.336	0.200

	mean POOL1	mean POOL2	mean POOL5	mean POOL6	mean POOL9	mean POOL10	mean POOL3	mean POOL4	mean POOL7	mean POOL8	mean POOL11	mean POOL12	log2FC	pval_ttest	pval_MW
taxon - genus
57495 - Dermacoccus	305.481	5917.258	13471.751	6928.560	4459.404	2394.527	423.370	3007.000	1343.009	1635.176	3198.117	2476.212	-1.470	0.091	0.180
2034 - Curtobacterium	287.578	1146.786	714.049	1274.796	1256.223	301.441	400.480	268.243	562.881	382.020	288.160	751.960	-0.908	0.085	0.240
2053 - Gordonia	695.644	2967.767	3917.834	4503.012	2424.041	483.676	683.715	935.358	1951.547	1333.037	644.310	2131.382	-0.965	0.122	0.240
1269 - Micrococcus	1110.814	8954.701	9975.120	4722.578	2855.938	10248.032	2225.037	6058.480	2774.230	2128.828	2784.971	5304.719	-0.832	0.145	0.240
149698 - Massilia	353.789	6106.866	2303.672	1937.320	86421.476	4726.679	1326.179	517.058	1073.470	10077.409	707.381	1935.530	-2.703	0.329	0.240
42255 - Rubrobacter	624.033	2149.939	1150.892	3435.797	2733.972	256.283	770.414	215.242	1529.386	963.869	559.830	1344.093	-0.943	0.162	0.310
28067 - Rubrivivax	1881.478	4131.116	1832.583	4063.519	4614.932	1775.669	2935.429	718.326	4460.710	1477.345	1070.475	2922.551	-0.430	0.350	0.310
57493 - Kocuria	1031.744	15190.352	54524.200	31542.960	8896.870	2415.975	2149.453	9120.696	5599.527	6025.127	2836.470	7362.656	-1.779	0.146	0.394
316612 - Methylibium	216.110	2643.853	1197.633	2133.420	2423.573	375.779	928.992	327.209	2010.186	434.434	336.476	1528.600	-0.692	0.296	0.394
391952 - Aquincola	951.821	4937.236	1132.556	2768.218	2224.077	876.308	3005.578	545.008	1318.198	520.111	832.077	2486.363	-0.566	0.387	0.394
338 - Xanthomonas	374.036	2138.231	1603.645	1703.963	674.235	610.614	512.292	339.224	2571.048	974.453	381.610	458.182	-0.440	0.513	0.394
2897311 - Fulvia	303.918	989.160	550.638	830.466	1007.416	267.445	471.783	289.887	612.793	398.651	440.920	711.754	-0.433	0.279	0.485
590 - Salmonella	131.286	937.761	675.219	1392.918	2223.327	690.424	378.414	331.981	797.150	13920.034	201.365	992.396	-0.580	0.357	0.485
165695 - Sphingobium	759.582	2048.377	4139.761	3742.727	3202.806	2728.723	1756.712	506.663	2614.973	46364.752	818.769	2891.203	1.725	0.412	0.485
561 - Escherichia	234.723	2897.806	1398.257	3607.204	3752.919	1138.062	655.062	637.035	1080.178	5030.515	411.988	1640.060	-0.463	0.535	0.485